Method of Organizing and Presenting Data in a Table

ABSTRACT

Methods of analyzing data are provided. An expert system receives input from at least a first source. Data is imported and analyzed by an expert system, wherein the expert system makes at least one first decision, which characterizes the data based on a rule base. The at least one first decision is displayable and modifiable by a first input from a first source. In response to the first input from the first source, the rule base may be re-applied to make at least one second decision, wherein the at least one second decision is different from the at least one first decision, or the at least one first decision may be accepted. The at least one first decision or the at least one second decision is then displayable and modifiable in response to a first input from a second source. In response to the first input from the second source, the rule base is either re-applied to make at least one third decision, wherein the third decision is different from the second decision, or either the first or second decisions arc accepted.

The present application is a divisional of U.S. patent application Ser.No. 11/560,566 and is related to the subject matter of the following twopending co-assigned applications: A METHOD OF ENHANCING EXPERT SYSTEMDECISION MAKING (U.S. application Ser. No. 11,560,580, now allowed), andA METHOD OF INTERACTION WITH AN AUTOMATED SYSTEM (U.S. patentapplication Ser. No. 11/560,601), all filed Nov. 16, 2006. The contentsof these three applications are herein incorporated by reference as totheir entire contents.

The U.S. government retains certain rights to this invention due tofunding provided by contract J-FBI-03-196 awarded by the Department ofJustice, Federal Bureau of Investigation.

TECHNICAL FIELD OF THE INVENTION

The technical field of the present invention relates to theinterpretation of data by an automated system and provides methods ofenhanced interaction with an automated system.

BACKGROUND

Automated systems are known for data analysis and display of data innumerous fields of interest. For example, in forensic science it iscommon for automated systems to assist forensic scientists in theidentification of a biological sample using DNA profiles. The terroristattacks of Sep. 11, 2001 placed huge demands on forensic scientists toidentify human remains from the collapsed World Trade Center buildings.In light of these demands, forensic scientists need more efficient andmore accurate tools to assist in the identification of biologicalspecimens when using automated. DNA profiling technology.

A forensic scientist may obtain a DNA profile from a sample obtainedfrom a personal effect of a missing person such as a toothbrush, razor,or comb, and searches for a match in a database containing DNA profilesfrom unknown biological specimens of a missing person or victim'sremains. One of many methods of obtaining a DNA profile are describedbelow. One can extract DNA from an unknown biological specimen by usingany DNA extraction technique. Many techniques for extracting DNA arewell known in the art. See, e.g., A. Gurvitz et al. AustralasBiotechnol. 1994 Mar-Apr;4(2):88-91; Ma et al. J Forensic Sci Soc. 1994October-December; 34(4):231-5; Laber et al. J Forensic Sci. 1992 March;37(2):404-24. After DNA extraction, an amplification procedure such asthe polymerase chain reaction can amplify the DNA with primers specificfor various regions of interest. Most commonly, the regions of interestcorrespond to the polymorphic short tandem repeat (STR) loci ofchromosomal DNA, which include D3S1358, vWA, FGA, D8S1179, D21S11,D18S51, D5S818, D13S317, D7S820, D16S539, TH01, TPOX, CSF1PO, Penta D,Penta F, D19S433, and AMEL. The amplification procedure may occur withfluorescently modified nucleotides, creating amplified DNA that isfluorescent. The fluorescent DNA is then separated by electrophoresisand the size of the DNA amplification product is determined subsequentlyby applicable software, allowing identification of the STR loci.

There is a need in the art, for example, for automated DNA SIR analysisand methods for reducing workload on forensic scientists. A highworkload, as is often the case after a terrorist incident, or a naturaldisaster, requires an extremely efficient workspace. Proper organizationand presentation of data is crucial to ensure proper interpretation ofresults. Thus, technologies are needed in forensic science in additionto other fields that can enhance interaction with an automated system,and provide more efficient methods of organization and presentation ofdata.

SUMMARY OF INVENTION

Several embodiments are discussed herein which provide various methodsof interaction with an automated system. In one embodiment, an item isorganized and presented by way of displaying a table, wherein the tabledisplays a plurality of data comprising analysis results characterizingthe at least one item, and wherein an analysis result is based on adecision made by an expert system according to a rule base. Input isaccepted from a source, wherein the input may cause the analysis resultsto be modified, and wherein the results may be modified by re-applyingthe rule base. In response to the input, an updated table is created,wherein the updated table comprises the modified analysis results. Thisupdated table is then displayed.

In another embodiment, items requiring input are compiled into a list,wherein the list comprises at least one item that requires input andanalysis results characterizing the at least one item. An analysisresult is based on a decision made by the expert system according to arule base. The list is displayable and input is accepted from a source,wherein the input might cause the analysis results to be modified. Theresults may be modified by re-applying the rule base. In response to theinput, an updated list is created which comprises modified analysisresults which characterize the at least one item.

In another embodiment, a method of enhancing expert system decisionmaking through interaction with at least one source is provided. Anexpert system is presented with at least one problem and the expertsystem makes at least one first decision that attempts to resolve the atleast one problem, wherein the first decision results from applying arule base for the at least one problem. Input is accepted from at leastone source and in response to the input the rule base might bere-applied. A second decision that attempts to resolve the at least oneproblem might result from re-applying the rule base. The second decisionis different from the first decision.

In another embodiment, a method of displaying and interacting with acollection of data is provided. The data comprise analysis results forat least one item. A first view displays a portion of the collection ofdata, wherein the first view comprises an expandable tree, theexpandable tree being displayable in a vertical frame within a projectwindow. A table characterizing the at least one item is displayable intabular form in a main frame adjacent to a vertical frame, wherein thetable displays the analysis results characterizing the at least one itemin response to selection within the expandable tree. The analysisresults of the table in this view are modifiable by input from a sourceto create an updated table, wherein the updated table comprises themodified analysis results. A second view of a portion of a collection ofdata is displayed, wherein the second view comprises a table, whereinthe table is organized in tabular form with a vertical columnrepresenting the at least one item. The table displays a plurality ofdata comprising analysis results characterizing the at least one item,wherein the analysis results of the table are modifiable by input from asource to create an updated table, wherein the updated table comprisesthe modified analysis results. The updated table is then displayable. Athird view of a portion of the collection of data comprises a list,wherein the list comprises at least one item requiring input from asource and analysis results of the at least one item. Analysis resultsare modifiable by the input to create an updated list, wherein theupdated list comprises the modified analysis results. The updated listis then displayable.

In an additional embodiment, a method of analyzing DNA electrophoresisdata is provided. An expert system receives input from at least a firstsource. Data is imported and analyzed by an expert system, wherein theexpert system makes at least one first decision, which characterizes thedata based on a rule base. The at least one first decision isdisplayable and modifiable by a first input from a first source. Inresponse to the first input from the first source, the rule base may bere-applied to make at least one second decision, wherein the at leastone second decision is different from the at least one first decision,or the at least one first decision may be accepted. The at least onefirst decision or the at least one second decision is then displayableand modifiable in response to a first input from a second source. Inresponse to the first input from the second source, the rule base iseither re-applied to make at least one third decision, wherein the thirddecision is different from the second decision, or either the first orsecond decisions are accepted.

Other embodiments of the invention are computer-readable media, whichstore computer executable instructions for performing any of thedisclosed methods.

BRIEF DESCRIPTION OF THE DRAWINGS

The present invention is illustrated by way of example, and not by wayof limitation, in the figures of the accompanying drawings.

FIG. 1 is a window illustrating an organized table comprising data.

FIG. 2 is a window showing an expandable item.

FIG. 3 is a window showing an organized table with options for expandingan item and providing input.

FIG. 4 MAP is a map showing the components of a project browser windowshowing an expandable tree (FIG. 4A), an organized table (FIG. 4C), anda data graph (FIG. 4B).

FIG. 5 is a window illustrating an organized table comprising data thathas been highlighted or marked for inspection.

FIG. 6 is a window illustrating an organized list comprising data thatrequires inspection.

FIG. 7 is a window showing an organized list with options for expandingan item and providing input.

FIG. 7.1 is a window displaying options for modifying customizationparameters for an expert system.

FIG. 7.2 is a window displaying options for modifying customizationparameters for an expert system.

FIG. 7.3 is a window displaying options for modifying customizationparameters for an expert system.

FIG. 7.4 is a window displaying options for modifying customizationparameters for an expert system.

FIG. 7.5 is an example graph displaying a potential “stutter peak.”

FIG. 8 is an example computer screen comprising one of many possibleconfigurations of simultaneously displaying a first (FIG. 8A), second(FIG. 8C), and third view (FIG. 8B) into a collection of data.

FIG. 9 is a flow diagram representing an example project flow of oneembodiment.

FIG. 10 illustrates a block diagram of a hardware environment that maybe used according to an illustrative embodiment of the invention.

DETAILED DESCRIPTION OF INVENTION

Referring to FIG. 1-10, here will now be described several embodimentsof methods for organizing, presenting, and analyzing items. By way ofexample only, organizing and presenting processed STR DNA data orinterpreting raw STR DNA data from capillary electrophoresis geneticanalyzers, such as the known models ABI 310 and ABI 3100 available fromApplied Biosystems, will be discussed in the context of the followingembodiments.

DNA profiles may comprise allele data for a defined set of loci. Eachlocus may contain one or more allele names (typically a positive integerpossibly followed by a decimal point and an integer from one throughthree, or one of the letters X or Y). DNA profile data may be obtainedfrom one or more experiments performed on a sample. The experiments canthen be organized into projects. Each experiment contains multiple timeseries of intensity measurements of optical fluorescence that correspondto the presence of dye-tagged DNA fragments at specific locations in acapillary. Detection of fluorescence peaks within a time series allowsone to determine DNA fragment size by using an associated sizingstandard. Each fluorescence peak is categorized by type and is assignedan allele value in association with a known allele ladder. In one aspectof these embodiments, it is essential to analyze raw data when analyzinga time series because the assignments made for each peak are determinedby peak attributes that can only be obtained from the raw data.Analyzing raw DNA peak data is described in more detail in PCTapplication 2006/029434 which claims priority to U.S. ProvisionalApplication 60/709,424, both of which are incorporated in theirentirety.

It is preferred that an expert, either a human expert or an expertsystem, be utilized to categorize and interpret DNA data. Experts useshape and other peak features, including the relationships betweenpeaks, to make decisions about whether peaks correspond to DNA alleles,or are artifacts due to the amplification process such as bleed-through,dye blobs, spikes, saturation, or other effects. In the followingembodiments, the term “decision” is meant to comprise any decision orobservation made on a peak, locus, profile, or specimen. An expert mayalso provide analysis for factors such as, but not limited to,contamination, procedural errors, temperature and electrical effects,and effects introduced by reagents and capillary aging.

The following discussion describes aspects in which an expert can beassisted by automated means, for example, by providing an organizedtable for displaying data, an organized list for displaying items thatrequire attention, a method of updating various views and displayingitems in a more efficient way. Also, methods are provided where externalsources can provide input which may cause an automated system to modifyits decisions.

In one embodiment, the term “item” as defined by the present inventionpertains to any project, specimen, data, such as Profile: K1 (P) Inj 1,or any piece of information which might be organized. A plurality ofitems may be analyzed when working with data such as DNA data and it isimperative to maintain a workstation with excellent organization. Forexample, FIG. 1 shows an item 1, which comprises specimen K1, whereinDNA from the specimen has been extracted, amplified via polymerase chainreaction, and separated via capillary electrophoresis. FIG. 1 displays atable 5 that is organized to present at least one item 1 to a source. A“source,” as referred to by the present embodiment, includes but is notlimited to an examiner, an expert, an expert system, a technician orother laboratory personnel, or a reviewer. In one embodiment, anexaminer will make any initial decisions and a reviewer will review theexaminer's work and provide feedback. Analysis results 10 characterizethe item 1, and are displayable within a table 5. Characterizing an itemis defined as providing information about an item. In this example,analysis results include but are not limited to allele data, such as:locus information for D3S1358, vWA, FGA, Amelogenin, D8S1179, D21S11,D18S51, D5S818, D13S317, D7S820,D16S539, TI-101, TPDX, and CSF1PO. Inaddition, information relating to the status of the item, informationpertaining to the item and information pertaining to processing of anitem are also considered analysis results. One aspect of the embodimentis to display the analysis results of an item in the table 5 within avertical column 15.

The item 1 may be expanded to display source or supporting data.Expanding an item, as taught by the present embodiment, comprisesdisplaying at least a second vertical column adjacent to the firstvertical column representing an item 1. FIG. 3 shows the second verticalcolumn 30 may display source or supporting data. Source data comprisesdata which gives rise to the analysis results. Supporting data mightinclude the source data as well as intermediate data that gives rise tothe final analysis results. In another embodiment, source data mightrefer to the actual origin of data. Expanding an item can beaccomplished by clicking on an item 1 or an analysis result 10 andselecting “Show Sources” 25. FIG. 2 shows clicking 20 on an item 1 toselect “Show Sources” 25. FIG. 3 shows that item 1 has been expanded toshow KitP and KitC 35 in second vertical column 30. FIG. 3 also showsselection of “17 peak #6,” 40 which allows a user to, for example,“Explore in Browser” 45, “View Observations,” “Reject/flag Item” 50,“view audit trail,” or “edit call” 55. Source data can be furtherexplored, or expanded in a project browser. FIG. 4 MAP shows a mapshowing the components of a window “project browser” 60, or the firstview, which is characterized by an expandable tree 65 (FIG. 4A)comprising at least one item, wherein the expandable tree is displayablein a vertical frame 70 within a project window 75. The project browsercomprises a table 80 (FIG. 4C) displayed in a main frame 82 adjacent tothe vertical frame 70. The main frame might comprise a plurality of dataand information including but not limited to a table 80 (FIG. 4C) ordata such as a graph 84 (FIG. 4B) representing a DNA peak which wasanalyzed by a DNA peak analysis program.

An additional feature of the current embodiment is that a system, suchas an expert system, which is involved in DNA peak analysis, might berequired to make a decision. To enhance this process, the system mayrequest input from a source. This embodiment provides a method forallowing a source, for example, a user, to enter input, which can causea decision to be modified by the expert system. Input is defined asaccepting (agreeing), rejecting 50, editing 55, defining as a mixture,or instructing a system, such as an expert system, to modify all or partof the analysis results 10. Input according to the present inventionalso comprises any action made by a source, which is acknowledged by asystem, for example, such as an expert system. In an environment wherean expert system makes decisions based on a rule base, the expert systemmay be programmed to accept input, to assist in the problem solving thatthe expert system is undertaking. The input has the ability to instructthe expert system to modify a decision by re-evaluating a rule base.Item 1 could contain allele data wherein the peak height is below 200RFU which, for example, might be outside a predetermined percentage ofanother peak's height. This result can, in this example, cause theexpert system to prompt the source for input. The source may then acceptor reject 50 the item causing a re-analysis of the rule base forassigning a peak. In one aspect, accepting a decision is accomplished bynot providing any input. In another aspect, accepting a decision occursby clicking on “accept observed item” 140 (FIG. 7). When a sourceaccepts or rejects a “decision” made by an expert system, it meansaccepting or rejecting a decision, observation, and all or any part ofthe analysis results made on the decision. A source accepting orrejecting a decision also comprises accepting or rejecting the item'sexistence and all or any part of the analysis results associated with anitem.

FIG. 5 shows that an expert system might request input by marking anitem for inspection. To assist in notifying the source that inspectionis required, the table 5 may contain a highlighted item 1 or ahighlighted analysis result 10. Highlighting 90, as defined by thecurrent invention, comprises changing the color of an item or ananalysis result of an item. Highlighting also comprises marking the itemwith a designated symbol to draw source attention, or by labeling thestatus of an item with “Inspect” 95. Marking the item may comprise theuse of a designated symbol such as an asterisk * 100, parentheses ()105, or a designated symbol such as ˜, !, @, #, $, %, ̂, &, *, }, {, or/. The table 1 may also be displayed in a window 110, which can belaunched or displayed at any time during the use of the program. Awindow is defined as a rectangular viewing area on a screen.

This embodiment accepts input from a source, which has the ability tocause a system, such as an expert system, to re-apply its rule base, orportions of its rule base, possibly coming to different conclusions,decisions, or observations from the initial conclusions, decisions, orobservations made by the system. Input may cause an analysis result tochange and therefore this change needs to be incorporated into a table5. An updated table is created when analysis results change and theupdated table is then displayable to the source. The term displaying, asused by the present invention, might involve displaying information oranalysis results to a user. It is also possible for information to bedisplayed to another computer system, such as an expert system via acommunications interface.

FIG. 6 shows an additional embodiment that involves a method ofenhancing access to at least one item, or a plurality of items that mayrequire input from a source. As described above, an expert may requireadditional expertise for certain problems, and therefore may requireinput to assist in resolving any problems. To enhance access to itemsrequiring input, a list 115 is compiled which comprises at least oneitem that requires input. The list may also display analysis results,portions of analysis results, or observations 135 pertaining to analysisresults. The list may be displayed in a window 120. Additionally, theuser inspection status can be displayed within the window. Userinspection status 125 displays information pertaining to the progress ofa source in reviewing, editing or providing feedback for items in thelist. Comments from a source may be displayable in a text field 130.Comments may include and are not limited to the source input, or adescription of the source input. The list also contains a state field132 which displays the current state of an item, e.g., Accept, Inspect,or Reject. A source field 134 is also displayable. The source fieldcomprises information relating to the source of the observation e.g.,Examiner, Reviewer, or an expert system. The source field displays theorigin of an observation made on an item.

FIG. 7 shows that the at least one item of the list may be expanded orexplored to display source and supporting data, wherein the source andsupporting data further characterize the item. A source can expandanalysis results that describe an item by selecting “explore inbrowser,” 150 as described in other embodiments. Analysis results aredisplayable from the table within a separate window, such as the projectbrowser 60.

The list accepts input as detailed in other embodiments, comprising butnot limited to, accepting an item 140, rejecting an item 155, ordefining the item as a mixture 145. A source selects an item in the listand is able to provide input, which might cause the analysis results tobe modified. The term “selects” refers to a selection made with an inputdevice such as a mouse, trackball, or stylus, as described below. Inresponse to accepting input, an updated list is created. The updatedlist comprises the modified analysis results characterizing the itemsthat required user input. The updated list is then displayable.

As described above, the expert system can be presented with at least oneproblem wherein the expert system attempts to resolve the at least oneproblem. Decisions can be made by the expert system based on a rule basefor the at least one problem. The expert system might be applied to alldetected fluorescent DNA peaks in at least one experiment of a project.For example, in a C++ implementation, all information related to the atleast one experiment can be associated with an object in an objectoriented software environment, such as a Run object. In a similarmanner, all project information can be associated with the object suchas a project object. This application of the expert system createsobservations associated with the peaks, which may be stored as C++Observation objects in a C++ Peak object. The expert system isimplemented using rules. Each rule can be thought of as an IF . . . THENclause, meaning that there is a set of conditions that are tested andmust be satisfied (the IF portion), in which case actions are taken(specified in the THEN portion). Each rule has subsequent requirementsthat are evaluated during the validation process to determine theeventual disposition of a rule's actions. For example, the Stutter Rule(as discussed below) checks to see if the primary peak is a callableallele after all dependency checks have been evaluated. In oneembodiment, these actions can create one or more C++ Observations objectand associate these objects with other objects such as a peak.

As an example, the expert system's rules are implemented by the C++runRule ( ) member functions that are associated with each type ofobservation (class derived from the C++ class Observation). These rulescan be grouped into the type of object with which they are associated:specimens, profiles, loci, runs, trace data, and peaks. At varioustimes, according to the stage of execution of the expert system and thetype of objects, the conditions associated with these rules are testedfor each of their corresponding observations, and the rules are fired(executed) when the conditions match.

This approach is different from that employed by most rule-based expertsystems.

Usually, a general-purpose execution engine is implemented often usingthe Rete algorithm (Charles Forgy. Rete: A fast algorithm for the manypattern/many object pattern match problem. Artificial Intelligence,19:17-37, 1982.) and operates upon a separately defined rule base ofif-then rules. The constraints of the DNA profile peak fittingapplication require a different approach, because a tight couplingbetween functions best implemented in a procedural language (C++) and bya traditional rule base was necessary.

The expert system's rules depend upon, and their operation can be tunedby adjusting a set of parameters that are maintained in a database witheach project. The parameters are specified by a C++ AnalysisParamsobject. These parameters can be easily customized to the needs of eachlaboratory or site. The expert system's decision processes will changeas a function of the selected parameters, and selected values must becarefully chosen using an informed process. Validation of the expertsystems' operation using tests with each laboratory's or site's datamust also be conducted to ensure that its operation is consistent withthe rules defined for that lab or site. The parameters may be modifiedfor the site, or for individual projects, by the person withAdministrator privileges using the procedure documented in theAdministrator's manual.

FIGS. 7.1 and 7.2 show that a source can change parameters to customizethe expert system rule base by accessing analysis properties. Forexample, a source can change the parameters for “background noise” 156by setting a “Noise peak amplitude threshold (RFU)” 157, “Noise peaknormalized MSE threshold” 158, “Noise peak area threshold (RFU)” 159,and “Noise peak skew threshold (absolute val)” 160. As another example,stutter peak parameters 161 can be changed by setting a “Saturatedstutter peak height threshold (% of primary)” 162. These parameters willdeter mine how the rule base of the expert system is applied.

Further, FIGS. 7.3 and 7.4 show that the interactive capabilities of theexpert system are modifiable by accessing application properties. Forexample, parameters such as “remember window layout” 163, “auto saveproject after data import” 164, “auto save project after analysis” 165,and “auto save interval (minutes)” 166 may be modified according topreference. As discussed above, highlighting may be utilized to drawattention to an item. The parameters that determine which colors areused for highlighting may be altered via application properties. Forexample, the background color of an item marked for inspection may beset to orange 167, and the text color of an item marked for inspectionmay be set to black 168. The colors may appear in the indicated colorboxes as set.

In a C++ implementation, the expert system's rules are executed from theC++

Specimen, Profile, Run, TraceData, Peak, and Locus classes, according tothe type of object being evaluated. When a rule fires (executes), anobservation is created and associated with the evaluated object. Theseobservations are usually marked as NOT_VALIDATED; each observationindicates that characteristics of the object indicate a possibleconclusion, for example that a peak is a stutter peak, but this must beverified by further automated analysis, and in certain circumstances,user review. After all initial observations are made, the validate( )methods of the various observations are called to determine whether eachobservation is VALID NOT_VALID, or AMBIGUOUS (requiring human review).This reconciliation process analyzes all of the observations of a givenpeak for dependencies on other peaks. All dependencies to an observationassociated with a peak are resolved before the peak (and itsobservations) are validated (marked VALID or NOT_VALID). Validationprocesses occur throughout the expert system's execution as required byboth the initial observations and dependencies among objects. Forexample, all observations that are initially marked NOT₁₃ VALIDATED areevaluated using the validate( ) function for that observation type toattempt a disposition. However, peaks may depend upon the status ofother peaks, so invalidation (NOT_VALID status) of a peak may causeinvalidation of other peaks. Thus, as the status of objects are changed,other objects must be checked. While most of this discussion pertainedto peaks, these processes are equally applicable to other objects and totheir classes when implemented in an object-oriented programminglanguage.

The expert system maintains information on dependencies andautomatically attempts validation wherever necessary. Once all thedependencies and observations are validated for each peak (or otherobject), the peak is assigned a type that is associated with the validobservations. (Peaks may have more than one type.) Circular dependenciesare possible and are automatically identified by the expert system. Whena circular dependency is detected and can not be automatically resolved,a flag is raised that causes notification to the source that manualreview, or input, is required. This may be considered marking an itemfor inspection. Objects at any level of the hierarchy can have aValidationState, and this state can be VALID only when all objects atlower levels descending from the object have been reconciled. (Note thatit is not necessary for all objects to have a VALID state; for example,a peak observation with a NOT-VALID state simply means that observationis not valid and thus does not propagate.) The hierarchy does however,allow the expert system software to attempt validation, orreconciliation, of all objects below a given object in the hierarchy byrequesting the validation of that object (and implicitly of all objectsbelow it).

The expert system's rules are grouped according to function. Thesoftware design of the DNA peak calling expert system can allowobservations to be associated with any object in the system, such asSpecimen, Profile, Run, TraceData, Peak, and Locus objects. Within eachgroup, rules are applied to every object of that type.

In this embodiment, by way of example only, a rule base may be appliedto classify “stutter peaks.” The Stutter Rule examines the relationshipbetween two peaks to determine if one of the peaks is a stutter peak ofthe other. In this discussion, the peaks will be referred to as Peak 1and Peak 2. The Stutter Rule is only checked for peak pairs where thelocation of Peak 1, in base-pairs, is less than the location of Peak 2.At least one of the peaks must be called, and neither peak can besaturated, in order for a stutter observation to be made.

Peak 1 may be classified as a stutter peak only if the difference in thelocations of the two peaks is within a specified range (in base-pairs),and if the height of Peak 1 is no more than a specified percentage ofthe height of Peak 2. This rule defines a region, relative to the mainpeak (Peak 2) in which Peak 1 must be (e.g., its location and height) inorder for a stutter observation to be made. FIG. 7.5 illustrates thisrelationship between the peaks. The maximum height of the stutter peakis specified as a percentage of the height of the primary peak, Peak 2.The lower and upper bounds on the stutter peak's location, inbase-pairs, are specified as an (negative) offset (to the left in theillustration) from the location of the main peak, Peak 2.

The specified range and percentage depend upon the locus of the peaks,so the

Stutter Rule first determines this locus. If Peak 1 is a called peak(has a locus and allele assignment), its locus assignment is used;otherwise, the locus assignment of Peak 2 is used. (One of the peaksmust be called in order for the Stutter Rule to be executed.)

The difference in the peaks' locations (calculated as the location ofPeak 1 less the location of Peak 2 and therefore a negative value) andthe peaks' heights are then used in a conditional expression todetermine if the rule is to fire and create a stutter observation (aStutterObservation object in the example C++ implementation). Thefollowing three inequalities must all be satisfied:

diff>=m_stutlow

diff<=m_stuthigh

(Peak 1)_(height) <=m_stutratio * (Peak 2)_(height)

where diff is the (negative) difference in the peaks' locations,m_stutlow is the specified smallest allowed (negative) difference inlocations between the stutter peak and its primary peak, m_stuthigh isthe specified largest allowed (negative) difference in locations betweenthe stutter peak and its primary peak, and m_stutratio is the maximumspecified ratio between the height of a stutter peak and the height ofits associated primary peak. These three parameters are specifiedindependently for each locus. The default values of these parameters aredetermined, first, by the initialization of the database, which occursin the example C++ implementation of the StrEspApp class in thedbInsertLocusRef ( ) member function, and second by any actions a user(with appropriate privilege) may take to change default values for ananalysis kit.

The expert system described in this embodiment has the capability tonotify a source to inspect, or enter input, to assist in the expertsystem decision making process. Based on various observations made bythe expert system, the expert system may require additional expertisefrom a source for at least one problem. Input from a source can triggerthe expert system to re-apply the rule base for the at least oneproblem. By way of example, the at least one problem is whether the DNApeak is a stutter peak or not. Initially, the system, or expert systemmay make a first decision which classifies a peak as a stutter peak;however, if predetermined criteria are not met, the system may requestuser input for assistance in the decision making. If a source disagreeswith the system, the source may provide input, which causes the rulebase to be re-applied to make a second decision. The second decisionmight then be different from the first decision.

Rather than re-apply the entire rule base, as applied to the DNA data,one aspect of this embodiment is to only re-apply those portions of therule base for the at least one problem that would result in a seconddecision to resolve the at least one problem. For example, if a sourcerejects an expert system made observation of “stutter peak,” the expertsystem will re-apply only the rule base as applied to stutter peaks, orpotential stutter peaks and to objects that depend upon the dispositionor decisions involving the stutter peak. It might not re-apply otherrule bases for different problems such as noise rule, broad peak rule,spike rule, duplicate allele rule, etc. One method in which thisselective re-application of the rule base can be achieved is by analysisof the dependencies among objects. These dependencies, for example, canbe represented by a graph, which can be analyzed to determine which rulebase needs to be re-applied. The advantage of selective re-applicationis performance; the software responds much more rapidly to user, orsource, actions or input.

FIGS. 8A-8C refer to an additional embodiment which involves a method ofdisplaying and interacting with a collection of data, wherein the datacomprise analysis results for the at least one item. A collection ofdata refers to the data that is being utilized in an analysis. This mayinclude an entire data set, or a fraction thereof. This embodimentincludes features of previously described embodiments. It is preferredto have at least one view, or a plurality of views, available to asource, wherein each view may contain the data organized in differentways. This can enhance efficiency and organization by allowing a sourceto arrange the plurality of views according to preference. For example.a first view (FIG. 8A) (169—project browser) comprises an expandabletree 170 comprising at least one item. The expandable tree isdisplayable in a vertical frame 175 within a project window. If an itemrequires input, as potentially determined by an expert system (describedabove), the item may be highlighted within a table 180. A table withinthe first view comprises analysis results characterizing the at leastone item and are displayable in a main frame 185 adjacent to thevertical frame 175. The table within the first view 190 displays theanalysis results characterizing the at least one item responsive toselection within the expandable tree 170. The analysis results displayedin the table 190 are modifiable by input from a source to create anupdated table within the first view, wherein the updated table comprisesthe modified analysis results. The computer system (detailed below) maythen display the updated table to the user in the first view 169.

A second view 195 (FIG. 8C) of a portion of the collection of datacomprises a table 225 which is organized in tabular form with a verticalcolumn 200 representing the at least one item. The table within thesecond view 225 displays a plurality of data comprising analysis results210 characterizing the at least one item, wherein the analysis resultsof the table are modifiable by input to create an updated table, whichcomprises the modified analysis results. The updated table is thendisplayable in the second view 195.

A third view (FIG. 8B) of a portion of the collection of data comprisesa list 215. The list contains at least one item that requires user inputin addition to analysis results of the at least one item. As describedin the above embodiments, the analysis results are modifiable by inputto create an updated list. The updated list comprises the modifiedanalysis results which may characterize the at least one item. Theupdated list is displayable in the third view 220.

It is advantageous to display at least two of the first, second, andthird views simultaneously in any possible configuration on a computerscreen. Each view may be displayed in a window and each view's locationon a screen can be modified by a source. Modifying a view's location ona screen might be done through moving the window via a mouse or withcontrol buttons on a keyboard. The F2 command may cause one view, suchas the second view, to move to the front of the display, while the F3command may cause another view, such as the third view, to move to thefront of the display. Any of the F commands located on a standardkeyboard might be assigned to any particular view, to enhance useraccess to a particular view.

In this embodiment, input from a source might be entered from within anyone of the plurality of views. Input, as described in the aboveembodiments, can cause analysis results to be modified, possibly byre-applying an expert system rule base. Further, analysis results can bemodified by a source in or from each view. For example, an input fromthe first view 165 may cause an updated table from the second view 195and an updated list from a third view 220 to be created. The updatedtable is displayable in the second view 195, and the updated list isdisplayed in the third view 220. The input from the first view 169 hasnot only caused the first view to contain updated analysis results, butalso has caused the additional views to contain the updated analysisresults. An input from the second view 195 may cause an updated tablefrom the first view and an updated list to be created. The updated tableis displayable in the first view 169 and the updated list is displayablein the third view 220. Again, the updated list and the updated tablecomprise analysis results that have been modified in response to theinput. Additionally, input from the third view, may cause an updatedtable from the first view and an updated table from the second view tobe created. An updated table is displayable in the first view 169, andan updated table is displayable in the second view 195. The updatedtables comprise analysis results that have been modified in response tothe input from the list in the third view 220. The modified results arenot only displayable within the view in which the input was accepted,the modified results are displayable in at least one other view as well.This ensures that a source can view and interact with the most accurate,up-to-date data available. Toggling between views allows viewing andinteracting with the most up-to-date and accurate data, presented in aplurality of combinations. All of the tables, lists, and information arealso displayable to another expert system, or computer system, via acommunications interface.

It is advantageous to maintain synchronization and consistency ofinformation in all views. The methods that utilize dependencyinformation in the expert system also provide an advantage by avoidingre-calculation of information or results that do not depend upon data orother information modified on input by a source. The dependency analysisand avoidance of unnecessary re-calculation enables the system torespond rapidly and in a timely manner to changes performed by a source.

An additional aspect of these embodiments begins with importing raw datainto a project. As described above, the data is analyzed by an expertsystem. The expert system makes at least one first observation ordecision characterizing the data based on a rule base. Withoutlimitation, an observation or decision will be called a “decision” inthe following description. A first source optionally logs into theprogram and reviews the at least one first decision. The first sourcecan provide a first input to the program that may correspond to thefirst decision. For example, the first input may indicate agreement ordisagreement with the first decision. As a second example, the firstinput may modify an analysis parameter that requires subsequentre-analysis of data by the expert system. As a third example, the firstinput may declare that a profile is a mixture and require reanalysis,causing the expert system to make at least one new decision.

The first decision is modifiable as a result of a first input from thefirst source, either directly by input from the source or indirectly. Anexample of a direct modification as a result of a first input is a newallele assignment of a peak provided directly by the first source. Anexample of an indirect modification as a result of a first input is amodification of a peak's classification or any other decision by theexpert system as a result of the first input. If necessary, the expertsystem re-applies the rule base and makes at least one second decision,wherein the second decision is different from the first decision. Thesecond decision is also modifiable as a result of input. A second sourceoptionally logs into the program and reviews the first and/or seconddecision. A second source can then enter a first input. As describedabove, possible inputs include, without limitation, acceptance orrejection of the first or second decision indicating agreement ordisagreement, respectively, with the input of the first source or thefirst or second decision. Input from the second source can also providefeedback to the first source or expert system.

In one example, the system may be configured to allow the second sourceto enter a first input comprising feedback, which might be agreement ordisagreement with the decision provided by the expert system and/orfirst source. In this configuration, only inputs provided by the firstsource can result in direct or indirect modification of a decision. Itis preferable that the expert system utilize input from the secondsource to determine whether the first and second source are in agreementwith each other. In this instance, the system can be configured toenhance the workflow process involving the two sources and system, andcan ensure that consensus between the two sources and the system beobtained. For example, agreement can be indicated by input from thefirst source and agreement with the re-analysis performed by the expertsystem.

In one embodiment, the first input from the second source might indicatedisagreement with the first source and cause the expert system tore-evaluate the rule base and make at least one third decision bymodifying the second decision. The third decision might be a directresult of the first input from the second source if the system isconfigured to allow this behavior. However, in another embodiment, thethird decision could be an indirect result of the first input from thesecond source. In this embodiment, the first source examines the atleast one first and/or second decisions, first input from the firstsource and/or the feedback and first input from the second source, andprovides a second input to the system. The expert system then makes atleast one third decision by modifying the second decision. Thus,feedback from the second source can indirectly modify the seconddecision. During this process, the first and second source mayoptionally choose to examine other information by, for example,examining data maintained by the system that is related to the decisionsand/or inputs. The objective of this process is for the first and secondsources to reach agreement and for a decision to be made that isconsistent with this agreement.

Alternatively, the second source might agree with the first sourcereview and the corresponding first or second decision by on an item, forexample, accepting the decision on an item or providing an indication ofconcurrence in feedback. In any scenario, once consensus has beenreached and there is agreement on all items, the project can befinalized.

In another embodiment, the expert system may be configured to interactwith one source. As described above, the source might include anexaminer, a reviewer, or another computer system, such as an expertsystem. In this scenario, the one source has the ability to modify theat least one decision made by the expert system. The source has theability to cause the expert system to re-evaluate, or re-apply the rulebase to make at least one second decision or the source may accept thefirst decision and finalize the project.

Example Project Flow

One example of possible project flow, as shown in FIG. 9, is discussedbelow.

The user who creates a project and performs the initial analysis andresults inspection will be referred to as the examiner and the user whoopens the inspected project for review will be referred to as thereviewer. After starting the program, the examiner optionally logs in250 to have his or her personal privileges and preferences automaticallyloaded by the DNA profile peak calling expert system. A new project canthen be created 260 with a specified name, workgroup, and optionally, apreferred reviewer. Raw capillary electrophoresis DNA files can beimported into the project 270. The examiner may select, for each file,several properties (such as run type, kit, specimen, extraction) ifthere is no consistent naming convention or information available fromother software, such as laboratory information management system (LIMS)otherwise, this information may be obtained from a file name conventionor another source such as a LIMS or database. The project is then readyto be analyzed 280. After an automated analysis by the expert system,the examiner resolves items 290 flagged for inspection in the examiner'sTDL (To Do List), also referred to as the third view, or the list, byaccepting or rejecting the listed items. Observations made by the expertsystem may also be overridden by the examiner by editing items in theproject, but the edited observation will be added to the TDL, alsoreferred to as the third view or the list, for review. Once all items inthe TDL have been inspected and resolved, the examiner notifies thereviewer 300 that the project is ready for review. The reviewer thenoptionally logs in to the expert system program and opens the project asa “reviewer.” This implies that the reviewer will review the actionstaken by the examiner. The reviewer inspects the reviewer's TDL, alsoreferred to as the third view or the list 320, which now lists theactions taken by the examiner on each item, and “accepts,” or provides“feedback” for each examiner decision. The reviewer may also “reject”the examiner's decisions. The reviewer and examiner are to discuss the“feedback” items and form a consensus on the appropriate actions to take330. Once both the examiner and reviewer agree on actions taken on allof the items listed in the TDL, also referred to as the third view orthe list, the project can be finalized by either user 340. A project isfinalized when the sources have accepted a decision. After finalization,projects cannot be modified, but a project can later be de-finalized byeither the reviewer or the examiner. Once it is finalized, reports canbe generated and profiles exported in a CMF format, importable to eitherCODIS or POPSTAT, or as a CODIS table for import to CODIS or acompatible LIMS environment 350. In one embodiment, a first source is anexaminer and a second source is a reviewer.

Implementation Mechanisms—Hardware Overview

Methods of the first embodiment and subsequent embodiments may beutilized in connection with computer readable media, which may beprovided for temporary or permanent storage in a personal computer orother computer known in the art. FIG. 10 is a block diagram thatillustrates a computer system 500 upon which at least one embodiment ofthe invention may be implemented. Computer system 500 includes a bus 502or other communication mechanism for communicating information, and atleast one processor 504 coupled with bus 502 for processing information.Computer system 500 also includes a main memory 506, such as a randomaccess memory (“RAM”) or other dynamic storage device, coupled to bus502 for storing information and instructions to be executed by processor504. Main memory 506 also may be used for storing temporary variables orother intermediate information during execution of instructions to beexecuted by processor 504. Computer system 500 may further include aread only memory (“ROM”) 508 or other static storage device coupled tobus 502 for storing static information and instructions for processor504. A storage device 510, such as a magnetic disk, optical disk,solid-state memory, or the like, may be provided and coupled to bus 502for storing information and instructions.

Computer system 500 may optionally be coupled via bus 502 to a display512, such as a cathode ray tube (“CRT”), liquid crystal display (“LCD”),plasma display, television, or the like, for displaying information to acomputer user. Alternatively, displayable information may be deliveredto a computer user or another computer system or computer program usinga communication interface 518. Delivery of information to anothercomputer is also referred to as displaying said information. An inputdevice 514, including alphanumeric and other keys, may be coupled to bus502 for communicating information and command selections to processor504. An optional type of user input device is cursor control 516, suchas a mouse, trackball, stylus, or cursor direction keys forcommunicating direction information and command selections to processor504 and for controlling cursor movement on display 512. This inputdevice typically has two degrees of freedom in two axes, a first axis(e.g., x) and a second axis (e.g., y), that allows the device to specifypositions in a plane. Alternatively, information and command selectionsmay be communicated to processor 504 using a communication interface518. Optionally, separate communication interfaces may be used todeliver information to a computer user or another computer system orcomputer program, and to communicate information and command selectionsto processor 504.

The invention is related to the use of computer system 500 forinterpreting raw data by an automated system and providing methods ofenhanced interaction with an automated system. According to oneembodiment of the invention, interpretation of raw data is provided bycomputer system 500 in response to processor 504 executing one or moresequences of one or more instructions contained in main memory 506. Suchinstructions may be read into main memory 506 from anothercomputer-readable medium, such as storage device 510. Execution of thesequences of instructions contained in main memory 506 causes processor504 to perform the process steps described herein. In alternativeembodiments, hard-wired circuitry may be used in place of or incombination with software instructions to implement the invention. Forexample, a field-programmable gate array (FPGA) or application-specificintegrated circuit (ASIC) may be used. Such a device can, for example,implement associative memory to aid in indexing, search, and retrievalof information stored in a database. Thus, embodiments of the inventionare not limited to any specific combination of hardware circuitry andsoftware.

The objects described in this invention, including for example objectscorresponding to the C++ classes Peak, Observation, AnalysisParams,TraceData, Locus, Specimen, Profile, and Run, are typically stored inmain memory 506. Other data structures may be used in place of theseobjects, in either an object-oriented or other programming and softwareexecution environment. Optionally, a database, such as MySQL, Oracle,Microsoft SQL Server, or RDB, may be used to store objects or other datastructures. In this case, it is advantageous to provide software toserialize and unserialize information stored in objects or other datastructure in order to convert the information into and from a form thatis more suitable for storage in a database. One example of thisconversion is performed in order to preserve links between objects ordata structures, which in a representation stored in main memory may bea memory address or pointer, and in a database may be a symbolicreference or other method of reference using, for example, a dictionaryor table of symbols. A second example of this conversion is to use arepresentation of numbers that is not specific to a particular processor504.

When a database is used to store objects or other data structures,regardless of representation, in addition to objects or data structuresstored in main memory, it can be advantageous to separate the functionsof database, expert system, and interface (or views) in to two or moreparts. In this case, for example, the database or expert system can beimplemented or executed on a second processor 504 or computer system 500in addition to the computer system that implements or executes theinterface. The multiple computer systems can communicate and coordinatetheir activities across any combination of a local area network, a widearea network, or, in a multiprocessor computer, shared memory or othercommunications channel. In particular, this allows multiple users tosimultaneously utilize the expert system or information stored in thedatabase. For example, a client/server configuration with a singledatabase is feasible, which allows a user to access his or her projectsfrom any client computer system. In this case, standard and well-knownmethods can be utilized to control and coordinate access and enforceconsistency across the server and all clients. Such a client/serverconfiguration can utilize user interface technologies such as thosediscussed elsewhere in this application, or web-enabled interfacetechnologies such as the hypertext markup language (HTML), JavaScript,Java, or a service-oriented architecture (SOA), all of which are wellknown in the art. In this manner, services can be provided by at leastone server over a large geographic area and to one or moreorganizations. When multiple servers are used, the servers maycommunicate to possibly share information and coordinate activities,including the ability to distribute workloads across multiple resources.

The term “computer-readable medium” as used herein refers to any mediumthat participates in providing instructions to processor 504 forexecution. Such a medium may take many forms, including but not limitedto, non-volatile media, volatile media, and transmission media.Non-volatile media includes, for example, optical or magnetic disks,solid state memories, and the like, such as storage device 510. Volatilemedia includes dynamic memory, such as main memory 506. Transmissionmedia includes coaxial cables, copper wire and fiber optics, includingthe wires that comprise bus 502. Transmission media can also take theform of acoustic or light waves, such as those generated during radiowave and infrared data communications.

Common forms of computer-readable media include, for example, a floppydisk, a flexible disk, hard disk, magnetic tape, or any other magneticmedium, a CD-ROM, any other optical medium, solid-state memory, punchcards, paper tape, any other physical medium with patterns of holes, aRAM, a PROM, and EPROM, a FLASH-EPROM, any other memory chip orcartridge, a carrier wave as described hereinafter, or any other mediumfrom which a computer can read. Various forms of computer readable mediamay be involved in carrying one or more sequences of one or moreinstructions to processor 504 for execution.

Computer system 500 may also include a communication interface 518coupled to bus 502. Communication interface 518 provides a two-way datacommunication coupling to a network link 520 that is connected to alocal network 522. For example, communication interface 518 may be anintegrated services digital network (“ISDN”) card or a modem to providea data communication connection to a corresponding type of telephoneline. As another example, communication interface 518 may be a networkcard (e.g., and Ethernet card) to provide a data communicationconnection to a compatible local area network (“LAN”) or wide areanetwork (“WAN”), such as the Internet or a private network. Wirelesslinks may also be implemented. In any such implementation, communicationinterface 518 sends and receives electrical, electromagnetic or opticalsignals that carry digital data streams representing various types ofinformation. For example, a forensic investigation may require a datacommunication connection to a database comprising at least DNA profiledata or other forensic information.

Network link 520 typically provides data communication through one ormore networks to other data devices. For example, network link 520 mayprovide a connection through local network 522 to a host computer 524 orto data equipment operated by an Internet Service Provider or privatenetwork service provider (“ISP”). ISP in turn provides datacommunication services through a packet data communication network suchas the worldwide network commonly referred to as the “Internet” 528 or aprivate network. An example of a private network is a secure datanetwork linking law enforcement agencies and used for transmission ofDNA and/or non-DNA information. Local network 522 and Internet 528 bothuse electrical, electromagnetic or optical signals that carry digitaldata streams. The signals through the various networks and the signalson network link 520 and through communication interface 518, which carrythe digital data to and from computer system 500, are exemplary forms ofcarrier waves transporting the information.

Computer system 500 can send messages and receive data, includingprogram code, through the network(s), network link 520 and communicationinterface 518. In the Internet example, a server 530 might transmit arequested code for an application program through Internet 528, hostcomputer 524, local network 522 and communication interface 518.

The received code may be executed by processor 504 as it is received,and/or stored in storage device 510, or other tangible computer-readablemedium (e.g., non-volatile storage) for later execution. In this manner,computer system 500 may obtain application code and/or data in the formof an intangible computer-readable medium such as a carrier wave,modulated data signal, or other propagated signal.

Computer system 500 can be configured using the methods of thisinvention to provide services across a network to forensic personnelhaving client computers capable of connection to the network. Theseservices can also be provided to other software, located in eithercomputer system 500 or a separate computer system connected by anetwork, network link, or communication interface to computer system500. The services can be protected using methods of authenticationand/or encryption that are known in the fields of computer science andcomputer security in order to ensure data are neither compromised nordisclosed and to trace all accesses to the data. The computer system 500and other associated information storage and communication componentscan be protected using devices and methods that are known in the fieldsof computer science and computer security, such as with firewalls,physical access controls, power conditioning equipment, and backup orredundant power sources. The information stored by computer system 500and computer-readable media can be further protected using backup orredundant information storage systems, such as those that are well-knownin the art. Examples include tape storage systems and RAID storagearrays.

Thus, there has been shown and described several approaches fororganizing and presenting data and methods for interacting with anautomated system. Approaches for re-applying an expert system rule basehave also been described which might be utilized in concert with themethods for organizing and presenting data. The following set of claimsshould not be deemed to be limited to the embodiments described above.Alternative embodiments may come to mind to one of ordinary skill in theart for application in alternative or later generation automatedsystems.

All patents, patent applications, and references cited in thisdisclosure are expressly incorporated herein by reference.

20-39. (canceled)
 40. A method of organizing and outputting results of aDNA analysis corresponding to a firing of rules of a rule base of a DNAprofile peak calling expert system, the expert system for automaticallyoutputting the DNA analysis results and corresponding rule firings ofsaid rule base of said expert system, the expert system comprisingcomputer readable media of a computer system, the computer systemincluding an input device, memory, a processor and an output device, themethod CHARACTERIZED BY: a. importing DNA electrophoresis data ofspecimen DNA obtained by a genetic analyzer for analysis by saidprocessor; b. outputting a table of data, said table comprising datarepresenting DNA analysis results based on a decision to fire a rulemade by the expert system responsive to a modifiable rule base, whereinthe rule base is modified, said modifiable rule base comprising a rulehaving a modifiable parameter value and a rule having a property ofbeing selectively fired by the expert system according to a type ofobject with which said rule is associated, by matching of a conditionassociated with said rule and, according to a stage of execution of saidexpert system, the output DNA analysis results including an observationrelated to one of concordance, a locus having three or more alleles, anoff ladder peak, a peak height imbalance and a peak falling in anoverlap region between loci responsive to the firing of a rule and aninput of one of acceptance or rejection from a first input sourcerequired of said observation for DNA analysis results requiringinspection; c. accepting modification data or rejection data from saidfirst input source, the modification or rejection data causing a changeof the DNA analysis results wherein the results are changed by one ofre-applying said modifiable rule base or applying a modified rule baseincorporating modified input parameter data; d. creating an updatedtable responsive to said first input source, the updated tablecomprising changed DNA analysis results; and e. outputting the updatedtable including the changed DNA analysis results wherein the changed DNAanalysis results correspond to one of a called DNA allele and a calledpeak.
 41. The method of claim 40 FURTHER CHARACTERIZED BY: theobservation is related to concordance.
 42. The method of claim 41FURTHER CHARACTERIZED BY: the observation is related to a locus having aconcordance error.
 43. The method of claim 40 FURTHER CHARACTERIZED BY:the observation is a locus has three or more alleles.
 44. The method ofclaim 40 FURTHER CHARACTERIZED BY: the observation is a locus has a peakheight imbalance.
 45. The method of claim 40 FURTHER CHARACTERIZED BY:the observation is a peak is an off ladder peak.
 46. The method of claim40 FURTHER CHARACTERIZED BY: the observation is a locus has a peakheight imbalance.
 47. The method of claim 40 FURTHER CHARACTERIZED BY:the observation is a peak is in an overlap region between loci.
 48. Amethod of organizing and outputting results of a DNA analysiscorresponding to a firing of rules of a rule base of a DNA profile peakcalling expert system, the expert system for automatically outputtingthe DNA analysis results and corresponding rule firings of said rulebase of said expert system, the expert system comprising computerreadable media of a computer system, the computer system including aninput device, memory, a processor and an output device, the methodCHARACTERIZED BY: a. importing DNA electrophoresis data of specimen DNAobtained by a genetic analyzer for analysis by said processor; b.outputting a table of data, said table comprising data representing DNAanalysis results based on a decision to fire a rule made by the expertsystem responsive to a modifiable rule base, wherein the rule base ismodified, said modifiable rule base comprising a first rule having amodifiable parameter value and a second rule having a property of beingselectively fired by the expert system according to a type of objectwith which said rule is associated, by matching of a conditionassociated with said rule and, according to a stage of execution of saidexpert system, the output DNA analysis results including an observationresponsive to the firing of a rule and an input of one of acceptance orrejection from a first input source required of said observation for DNAanalysis results requiring inspection, said first and second rules beingselected from a list comprising two of background noise, spike, broadpeak, duplicate allele, bleed-through peak, pull-up peak, stutter peak,—A peak and global filter rules; c. accepting modification data orrejection data from said first input source, the modification orrejection data causing a change of the DNA analysis results wherein theresults are changed by one of re-applying said modifiable rule base orapplying a modified rule base incorporating modified input parameterdata; d. creating an updated table responsive to said first inputsource, the updated table comprising changed DNA analysis results; ande. outputting the updated table including the changed DNA analysisresults wherein the changed DNA analysis results correspond to one of acalled DNA allele and a called peak.
 49. The method of claim 48 FURTHERCHARACTERIZED BY the background noise rule being selectably enabled. 50.The method of claim 49 FURTHER CHARACTERIZED BY the background noiserule having a modifiable noise peak threshold parameter.
 51. The methodof claim 50 FURTHER CHARACTERIZED BY the modifiable noise peak thresholdcomprising one of a noise peak amplitude threshold, a noise peaknormalized MSE threshold, a noise peak area threshold and a noise peakskew threshold.
 52. The method of claim 48 FURTHER CHARACTERIZED BY thestutter peak rule being selectably enabled according to one of a stutterpeak detection rule and a saturated stutter peak detection rule.
 53. Themethod of claim 52 FURTHER CHARACTERIZED BY the stutter peak rule havinga modifiable stutter peak height threshold parameter.
 54. The method ofclaim 52 FURTHER CHARACTERIZED IN THAT once selectably enabled, thestutter peak rule is executed if one of a primary peak and a stutterpeak is assigned a locus and allele.
 55. The method of claim 54 FURTHERCHARACTERIZED IN THAT the stutter peak rule is executed based on acalculation of the difference in location of the primary peak and thestutter peak.
 56. The method of claim 55 FURTHER CHARACTERIZED IN THATthe stutter peak rule calculates the height of the primary peak and thestutter peak and determines a height ratio.
 57. A method of organizingand outputting results of a DNA analysis corresponding to a firing ofrules of a rule base of a DNA profile peak calling expert system, theexpert system for automatically outputting the DNA analysis results andcorresponding rule firings of said rule base of said expert system, theexpert system comprising computer readable media of a computer system,the computer system including an input device, memory, a processor andan output device, the method CHARACTERIZED BY: a. importing DNAelectrophoresis data of specimen DNA obtained by a genetic analyzer foranalysis by said processor; b. outputting a table of data, said tablecomprising data representing DNA analysis results based on a decision tofire a rule made by the expert system responsive to a modifiable rulebase, wherein the rule base is modified, said modifiable rule basecomprising a first rule having a modifiable parameter value and a secondrule having a property of being selectively fired by the expert systemaccording to a type of object with which said rule is associated, bymatching of a condition associated with said rule and, according to astage of execution of said expert system, the output DNA analysisresults including an observation related to one of concordance, a locushaving three or more alleles, an off ladder peak, a peak heightimbalance and a peak falling in an overlap region between lociresponsive to the firing of a rule and an input of one of acceptance orrejection from a first input source required of said observation for DNAanalysis results requiring inspection, said first and second rules beingselected from a list comprising two of background noise, spike, broadpeak, duplicate allele, bleed-through peak, pull-up peak, stutter peak,—A peak and global filter rules; c. accepting modification data orrejection data from said first input source, the modification orrejection data causing a change of the DNA analysis results wherein theresults are changed by one of re-applying said modifiable rule base orapplying a modified rule base incorporating modified input parameterdata; d. creating an updated table responsive to said first inputsource, the updated table comprising changed DNA analysis results; ande. outputting the updated table including the changed. DNA analysisresults wherein the changed DNA analysis results correspond to one of acalled DNA allele and a called peak.
 58. The method of claim 57 FURTHERCHARACTERIZED BY: the observation is related to concordance.
 59. Themethod of claim 57 FURTHER CHARACTERIZED BY the background noise rulebeing selectably enabled and having a modifiable noise peak thresholdparameter.